Analyzing Twitter-Based Social Networks of Support Communities for Hispanic and African American Family Caregivers of Persons with Dementia.

We applied social network analysis to compare Hispanic and Black dementia caregiving networks on Twitter that were established as part of a clinical trial from January 12, 2022, to October 31, 2022. We extracted Twitter data from our caregiver support communities (N=1980 followers, 811 enrollees) via the Twitter API and used social network analysis software to compare friend/follower interactions within each Hispanic and Black caregiving network. Analysis of the social networks revealed that enrolled family caregivers without prior social media competency had overall low connectedness compared to both enrolled and non-enrolled caregivers with social media competency, who were more integrated into the communities that developed through the clinical trial, partly due to their ties to external dementia caregiving groups. These observed dynamics will help to guide further social media-based interventions and also support the observation that our recruitment strategies effectively enrolled family caregivers with various levels of social media competency.

Comparing Emotional Valence Scores of Twitter Messages from Human Coding and Machine Learning Algorithms Among Hispanic and African American Family Caregivers of Persons with Dementia.

We compared emotional valence scores as determined via machine learning approaches to human-coded scores of direct messages on Twitter from our 2,301 followers during a Twitter-based clinical trial screening for Hispanic and African American family caregivers of persons with dementia. We manually assigned emotional valence scores to 249 randomly selected direct Twitter messages from our followers (N=2,301), then we applied three machine learning sentiment analysis algorithms to extract emotional valence scores for each message and compared their mean scores to the human coding results. The aggregated mean emotional scores from the natural language processing were slightly positive, while the mean score from human coding as a gold standard was negative. Clusters of strongly negative sentiments were observed in followers' responses to being found non-eligible for the study, indicating a significant need for alternative strategies to provide similar research opportunities to non-eligible family caregivers.

Analyzing Topics and Sentiments from Twitter to Gain Insights to Refine Interventions for Family Caregivers of Persons with Alzheimer's Disease and Related Dementias (ADRD) During COVID-19 Pandemic.

We randomly extracted Tweets mentioning dementia/Alzheimer's caregiving-related terms (n= 58,094) from Aug 23, 2019, to Sep 14, 2020, via an API. We applied a clustering algorithm and natural language processing (NLP) to publicly available English Tweets to detect topics and sentiment. We compared emotional valence scores of Tweets from before (through the end of 2019) and after the beginning of the COVID-19 pandemic (2020-). Prevalence of topics related to caregiver emotional distress (e.g., depression, helplessness, stigma, loneliness, elder abuse) and caregiver coping (e.g., resilience, love, reading books) increased, and topics related to late-stage dementia caregiving (e.g., nursing home placement, hospice, palliative care) decreased during the pandemic. The mean emotional valence score significantly decreased from 1.18 (SD 1.57; range -7.1 to 7.9) to 0.86 (SD 1.57; range -5.5 to 6.85) after the advent of COVID-19 (difference -0.32 CI: -0.35, -0.29). The application of topic modeling and sentiment analysis to streaming social media provides a foundation for research insights regarding mental health needs for family caregivers of a person with ADRD during COVID-19 pandemic.

Applying Social Network Analysis to Compare Dementia Caregiving Networks on Twitter in Hispanic and Black Communities.

We applied social network analysis (SNA) on Tweets to compare Hispanic and Black dementia caregiving networks. We randomly extracted Tweets mentioning dementia caregiving and related terms from corpora collected daily via the Twitter API from September 1 to December 31, 2019 (initial corpus: n = 2,742,539 Tweets, random sample n = 549,380 English Tweets, n= 185,684 Spanish Tweets). After removing bot-generated Tweets, we first applied a lexicon-based demographic inference algorithm to automatically identify Tweets likely authored by Black and Hispanic individuals using Python (n = 114,511 English, n = 1,185 Spanish). Then, using ORA, we computed network measures at macro, meso, and micro levels and applied the Louvain clustering algorithm to detect groups within each Hispanic and Black caregiving network. Both networks contained a similar proportion of dyads and triads (Hispanic 88.2%, Black 88.9%), while the Black caregiving network included a slightly larger proportion of isolates (Hispanic 0.8%, Black 4.0%). This study provides useful baseline information on the composition of existing large groups and small groups. In addition, this work provides useful guidance for future recruitment strategies and the design of social support interventions regarding emotional needs for Hispanic and Black dementia caregivers.

Application of Machine Learning Techniques to Examine Social Service Needs Among Hispanic Family Caregivers of Persons with Dementia.

We applied machine learning algorithms to examine the relationship between demographics and outcomes of the social work services used by Hispanic family caregivers of persons with dementia recruited for a clinical trial in New York City. The social work service needs were largely concentrated on instrumental support to gain access to the healthcare system rather than other concrete services (e.g., housing or food programs) or to address psychological needs among the caregivers with relatively higher income. A finding from the machine learning approach was that among those who receive medical-related social work services, frequent users (≥10 times) with high family friend support(>4) were more likely than frequent users without such support to have their issues resolved (Accuracy: 81.9%, AUC: 0.82, F-measure: 0.86 by J48). Even though half of the participants received social work services multiple times, the needs of the caregivers remained unmet unless they sought social work services frequently (more than ten times).

Comparative Effectiveness of 2 Interventions for Hispanic Caregivers of Persons with Dementia.

OBJECTIVES: To compare the effectiveness of 2 caregiver interventions with known efficacy: the Resources for Enhancing Caregiver Health-Offering Useful Treatment (REACH-OUT) and the New York University Caregiver Intervention (NYUCI). DESIGN: 1:1 randomized pragmatic trial. SETTING: New York City. PARTICIPANTS: Informal Hispanic caregivers of persons with dementia (N=221; mean age 58.2, 82.8% female, 63.3% adult children, 31.7% spouses). INTERVENTION: Participants were randomized to 6 months of NYUCI (n=110) or REACH-OUT (n=111), balanced on characteristics at baseline. All participants were referred for social supportive services. MEASUREMENTS: The primary outcomes were changes between baseline and 6 months in depressive symptoms, measured using the Geriatric Depression Scale (GDS), and caregiver burden, measured using the Zarit Caregiver Burden Scale (ZCBS). RESULTS: There were no differences in outcomes between NYUCI and REACH-OUT. Both interventions showed a reduction in burden (REACH-OUT: 5.2 points, 95% confidence interval (CI)=2.2-8.1, p<.001; NYUCI: 4.6-points, 95% CI=1.7-7.5, p=.002). There were no significant changes on the GDS. Effects for the ZCBS were significant only for spouses and older caregivers. CONCLUSION: Although there were no significant intervention group differences, both interventions resulted in significantly reduced burden for Hispanic caregivers at 6 months, particularly for spouses and older caregivers.

Characteristics and mental health of Hispanic dementia caregivers in New York City.

BACKGROUND: Dementia prevalence and related caregiving burden are increasing, particularly among Hispanics. We studied the characteristics and mental health of Hispanic caregivers in New York City. METHODS: We recruited 139 Hispanic family caregivers. We collected data on sociodemographic characteristics and predictors of caregiver burden, measured with the Zarit Caregiver Burden Scale, and depressive symptoms, measured with the Geriatric Depression Scale. RESULTS: The mean age was 59.3 ± 10.4 years. The majority of caregivers were daughters and earned less than US$30 000 a year. In multivariate analyses with linear regression, lower satisfaction with social networks was associated with higher caregiver burden and a greater number of depressive symptoms. Higher dementia severity was associated with higher caregiver burden, while higher caregiver comorbidities were associated with higher depressive symptoms. CONCLUSIONS: Caregiver comorbidities and satisfaction with social support may be targets for intervention that could improve caregiver burden and depressive symptoms among Hispanic caregivers.